Air quality monitoring system and method

ABSTRACT

In an illustrative configuration, a method for monitoring air quality is disclosed. The method includes accepting analyte gas into a cell and reflecting light rays into the analyte gas repeatedly across the cell into at least one sensor. The light scattered by particulate matter in the analyte gas and amount of spectra-absorption due to presence of a gaseous chemical is then measured. Based on the determined amount of spectra-absorption and the measured scattered light the gaseous chemical is then measured.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of a patent application Ser. No.18/113,311, filed on Feb. 23, 2023, and entitled “Air Quality MonitoringSystem and Method,” which is a continuation of patent application Ser.No. 17/962,171, filed on Oct. 7, 2022, and entitled “Air QualityMonitoring System and Method,” (now U.S. Pat. No. 11,604,094, issued onMar. 14, 2023), which is a divisional of patent application Ser. No.17/716,326, filed on Apr. 8, 2022, and entitled “Air Quality MonitoringSystem and Method” (now U.S. Pat. No. 11,467,032 issued on Oct. 11,2022), which is a divisional of U.S. patent application Ser. No.17/540,497, filed on Dec. 2, 2021, and entitled “Air Quality MonitoringSystem and Method,” (now U.S. Pat. No. 11,346,717, issued on May 31,2022), which is a Continuation of patent application Ser. No.16/953,908, filed on Nov. 20, 2020, and entitled “Air Quality MonitoringSystem Enhanced Spectrophotometric Chemical Sensor” (now U.S. Pat. No.11,193,822, issued on Dec. 7, 2021), which is a division of U.S. patentapplication Ser. No. 16/823,205, filed on Mar. 18, 2020, and entitled“Air Quality Monitoring System and Enhanced Spectrophotometric ChemicalSensor,” (now U.S. Pat. No. 10,876,890, issued on Dec. 29, 2020), whichis further a divisional U.S. patent application Ser. No. 16/188,793,filed on Nov. 13, 2018, and entitled “Air Quality Monitoring System andEnhanced Spectrophotometric Chemical Sensor” (now U.S. Pat. No.10,634,558, issued on Apr. 28, 2020).

A portion of the disclosure of this patent document contains material,which is subject to copyright and/or mask work protection. The copyrightand/or mask work owner has no objection to the facsimile reproduction byanyone of the patent document or the patent disclosure, as it appears inthe Patent and Trademark Office patent file or records, but otherwisereserves all copyright and/or mask work rights whatsoever.

BACKGROUND

Air quality is one of the most important factors that can affect thehealth of a population. Countries around the world spend significantresources on monitoring air quality and controlling air pollution. Oneof the major problems is that instruments that can accurately monitorair quality are expensive and typically require expertise to operateproperly. Currently, air quality monitoring is mainly performed bygovernment agencies and dedicated organizations using specializedinstrumentation. As a result, general air quality data often does notprovide the fidelity necessary to pinpoint issues in a scale smallerthan a regional level. Because air quality monitoring instruments are soexpensive, most people typically do not have the means to obtain thedata needed to identify air quality issues on an individual basis.

Described herein is an air quality monitoring system that enables a widescale deployment of monitors with enough accuracy for meaningful andactionable data. In one aspect, an advanced technique is used tocalibrate low-precision gaseous chemical sensors to obtain accuratemeasurements by cross-calibrating those sensors to correct sensitivitiesto parameters that cause errors to measurements of targeted chemicals.In another aspect, air quality measurements are used to identify sourcesof chemicals in a localized level by accounting for local conditionsusing data such as ambient condition data and user-provided data aboutthe local environment. In yet another aspect, a gaseous chemical sensorwith an improved encasement having a cell for reflecting and lengtheninglight path is provided to reduce the limitations and enhance theaccuracy of a conventional spectrophotometric gaseous chemical sensor.

The system and components described herein reduce the resources (e.g.,instrument setup time, cost, expertise) that are needed to deploy alarge-scale air quality monitoring system and to increase fidelity ofair quality data. Reducing the need for resources also enables new waysof gathering air quality data, such as by crowd-sourcing data frominstruments deployed by average users. The invention also serves todemocratize air quality monitoring by making air quality instrumentationand analysis affordable to individual users.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying figures of the drawing, which are included to provide afurther understanding of general aspects of the system/method, areincorporated in and constitute a part of this specification. Theseillustrative aspects of the system/method, and together with thedetailed description, explain the principles of the system. No attemptis made to show structural details in more detail than is necessary fora fundamental understanding of the system and various ways in which itis practiced. The following figures of the drawing include:

FIG. 1 shows an example of an air quality monitoring system.

FIG. 2 shows an example air quality monitor and some example componentsthat may be included.

FIG. 3 shows a flow chart of an example cross-calibration method forcalibrating a gaseous chemical sensor in an air quality monitor.

FIG. 4A shows an overview of sensor calibration.

FIG. 4B shows an overview of sensor calibration update.

FIG. 5 shows a flow chart of an example source determination method.

FIG. 6 shows an example of an example gaseous chemical sensor withexample components.

In the appended figures, similar components and/or features may have thesame reference label. Further, various components of the same type maybe distinguished by following the reference label by a dash and a secondlabel that distinguishes among the similar components. If only the firstreference label is used in the specification, the description isapplicable to any one of the similar components having the same firstreference label irrespective of the second reference label. Where thereference label is used in the specification, the description isapplicable to any one of the similar components having the samereference label.

DETAILED DESCRIPTION

Illustrative configurations are described with reference to theaccompanying drawings. Wherever convenient, the same reference numbersare used throughout the drawings to refer to the same or like parts.While examples and features of disclosed principles are describedherein, modifications, adaptations, and other implementations arepossible without departing from the spirit and scope of the disclosedconfigurations. It is intended that the following detailed descriptionbe considered as exemplary only, with the true scope and spirit beingindicated by the following claims.

FIG. 1 shows an example of an air quality monitoring system 110, whichhandles air quality data from different sources. As illustrated in FIG.1 , air quality monitoring system 110 may include an air quality dataprocessing module 121, a plurality of air quality monitors 132-134,reference monitors 137 and environmental monitors 139. Air qualitymonitors 132-134 can include one or more chemical sensors configured todetect and measure chemicals, such as ozone, nitrogen oxide, carbondioxide, sulfur dioxide, volatile organic compounds, methane or otherhydrocarbons, and other chemicals in gaseous state (these are hereinbeing described as gaseous chemicals), as well as one or more particlesensors configured to detect and measure the presence of suspendedparticles in air such as dust, smoke, pollen, or soot (these are hereindescribed as particulate matter or PM). Air quality monitors 132-134 mayinclude an enhanced gaseous chemical sensor having a multi-pass cell forlight rays, as will be described in more detailed below, such as inconjunction with FIG. 6 . Air quality monitors 132-134 may be located atmultiple different locations. For example, multiple monitors may belocated around a sizable area, such as a county, a city or aneighborhood. Several instruments may also be located within a buildingor a dwelling.

Reference monitors 137 include precision gaseous chemical sensors andare configured to provide measurements for use in calibrating thegaseous chemical sensors in air quality monitors 132-134. Environmentalmonitors 139 are configured to measure environmental conditions, such ashumidity, temperature, atmospheric pressure, air density, light,location, wind speed, and the like.

Air quality data processing module 121 is configured to communicate withair quality monitors 132-134, reference monitors 137, and environmentalmonitors 139. For example, air quality data processing module 121 mayreceive data from these monitors, such as measurements. Air quality dataprocessing module 121 may also transmit data to these monitors, such asproviding calibration data. Air quality data processing module 121 cancorrect measurements from air quality monitors 132-134 usingcross-calibration factors, as will be explained below. Air quality dataprocessing module 121 is also configured to process the data frommonitors and perform analysis to calculate or infer additional airquality data such as the amount of various gaseous chemicals in variouslocations, sources of those gaseous chemical, and recommendations basedon elicited requirements or preferences of end users. Air quality dataprocessing module 121 is configured to communicate with mobile devices152, computing devices 151 and server devices 153 to receive data andprovide received, calculated, and inferred air quality data. Forexample, air quality data processing module 121 may receive user-inputdata and use that data to derive additional air quality data relevant tothe area of analysis. Air quality data processing module 121 is alsoconfigured to communicate with other sources of data such as reportingsystem 154 and weather stations 155. Air quality data processing module121 may be implemented in any appropriate physical or virtual computingplatform (such as a networked server), and may operate and act throughany suitable interface (such as a cloud computing platform).

Air quality monitoring system 100 may also be configured to processincoming data to provide a variety of outputs. For example, air qualitymonitoring system may analyze measurements from air quality monitors132-134 to determine sources of the gaseous chemicals being detected.Air quality monitoring system may provide actionable steps to affect thechemical sources, such as ways to reduce the release of those chemicalsor ways to minimize exposure to those chemicals, making use of statedpreferences or user requirements, and/or ancillary (e.g. topological,geological, meteorological, demographic) datasets relevant to the areaof investigation.

FIG. 2 shows an example air quality monitor 200 (such as air qualitymonitors 132-134 in FIG. 1 ) and some example components that may beincluded. Air quality monitor 200 may include processor module 211,memory 213, communication module 215, and one or more gaseous chemicalsensors, such as gaseous chemical sensors 221-223, and environmentalsensor 230. Processor module 211 processes computing tasks and controlsother components. The computing tasks may include data calibration.Memory 213 stores data, such as measurement data from gaseous chemicalsensors 221-223 and calibration data such as cross-calibration factors.Gaseous chemical sensors 221-223 are configured to measure gaseouschemicals in analyte gas, such as gas under sampling by air qualitymonitor 200. Environmental sensor 230 measures environment conditions,such as temperature, pressure, humidity, location, wind speed, and thelike. Communication module 215 handles communication with other devices.For example, communication module 215 may handle communication betweenair quality monitor 200 and air quality data processing module 121,other air quality monitors, user-devices such as mobile devices 151 andcomputing devices 152, and the like. Communication module 215 maycommunicate through any of a variety of wired and wireless mechanisms,such as Wi-Fi, Bluetooth, mobile networks, and the like. Air qualitymonitor 200 may also be configured to measure time, position and otherrelevant information for computing devices.

Gas Sensor Calibration Process

Air quality monitoring system 110 is configured to increase the accuracyof low-precision gaseous chemical sensors through cross-calibration.Operators of air quality monitoring system 110 may implement across-calibration method 300 as shown in FIG. 3 . This cross-calibrationmethod 300 can improve the accuracy of low-precision gaseous chemicalsensors, which are sensitive to both their target gas as well asadditional parameters, including (but not limited to) other gases,changes in environmental conditions (wind, pressure, humidity/moisture),or radio waves. Cross-calibration method 300 calculates sensitivity ofone of the gaseous sensors to the target gas as well as confoundingfactors, and deduce the true value of the target gas by, for example,placing air quality monitors with low-precision gaseous chemical sensorsnext to a reference monitor with high-precision sensors.

Example equations for calculating cross-calibration factors and errorsfor calibration are shown below. According to the cross-calibrationmethod, a low-precision sensor tasked with measuring a gas concentrationX is sensitive to additional parameters y1, y2, yn, as illustrated inequation 1. In practice, one or more air quality monitors and one ormore reference monitors may be used. The air quality monitors withlow-precision gas sensors are placed next to reference monitors withhigh precision gas sensors which are not sensitive to these additionalparameters. The calibration method determines the concentration of gas Xas a function of the measured concentration y0 and additional parametersy1, y2, yn using equation 1. Coefficients a0, a1 . . . , an aredetermined where these coefficients represent sensitivity of the lowprecision sensor to parameter y1, y2, . . . , yn.

Y ₀ =a ₀ X+a ₁ y ₁ +a ₂ y ₂ + . . . +a _(n) y _(n)  Equation 1:

After the air quality monitors are deployed in the network, an airquality monitor with low-precision gaseous chemical sensors may beplaced next to a reference monitor with high-gaseous chemical sensors.The error between the high-precision monitor and the low-precisionprecision monitor may be calculated using equation 2 below:

ϵ=X′−1/a ₀(y ₀ −a ₁ y ₁ − . . . −a _(n) y _(n)  Equation 2:

If the error is nonzero, the cross-calibration method is performed forthis pair of low-precision sensor and reference sensor. The differencein original and updated parameters y1, yn is reported and then pushed todevices on the network. For a naive implementation, only the error ϵ isapplied as a correction to the network of air quality monitors withsimilar low-precision gaseous chemical sensors, though more involvedmethods may be used.

In the air quality monitor network, the cross-calibration method 300 canbe implemented by first placing each of the air quality monitors next toa reference monitor to calculate coefficients of the parameters forcalibration. As shown in step 312 and illustrated in FIG. 4A,cross-calibration begins by co-locating low-precision gaseous chemicalsensors and high-precious gaseous chemical sensors. These sensors can beco-located using any of a variety of different configurations, such asby themselves, while incorporated in air quality monitors and referencemonitors, a mix of different configurations, and the like.

At step 314, cross-calibration factors are determined. Cross-calibrationfactors may include coefficients for parameters associated with thelow-precision gaseous chemical sensors. These cross-calibration factorsmay be implemented in any of a variety of ways and data structures, suchas being simple values of the coefficients, combining the coefficientswith the parameters, as an array of values, and the like. Thesecross-calibration factors may be used by individual air qualitymonitors, air quality data processing modules, other systems tocalibrate and correct measurements of low-precision sensors of airquality monitors, and the like.

At step 316, the air quality monitors are deployed in the network. Asshown in FIG. 4B, one or more selected low-precision gas sensors 433 maybe kept in proximity to one or more reference monitors 421 for updatingcross-calibration after deployment.

Cross-calibration method 300 can calculate updated coefficients inreal-time and apply that update to the network of air quality monitors.For example, at step 318, an error is computed to determine if thecross-calibration factors require an update. The error may be calculatedusing example equation 2 and as discussed above.

At decision step 321, a determination is made whether the error exceedsa threshold. An error that exceeds a threshold indicates that thecross-calibration factors may require an update. If the error exceedsthe threshold and an update is needed, method 300 moves to step 332where the cross-calibration factors are updated based on measurementsfrom the low-precision gaseous chemical sensor and high-precisiongaseous chemical sensor that were co-located after deployment forcalibration updating purpose, as discussed in connection with step 316and FIG. 4B. At step 341, the updated cross-calibration factors areprovided to the air quality monitors of the network and the processends.

Back at decision step 321, if the error is below the threshold, method300 moves to step 333 where cross-calibration factors of air qualitymonitors of the network are up-to-date and the process ends.

Cross-calibration method 300 above may be implemented in any of avariety of different ways by different devices in any combinations. Forexample, air quality data processing module may implement this processto initially calibrate the air quality monitors by determining thecross-calibration factors and then updating the air quality monitors ina periodic basis. The air quality data processing module may correctdata received from the deployed air quality monitors based on theupdated cross-calibration factors, push these updated factors to thedeployed air quality monitors so that the monitors can update the databefore sending to the air quality data processing module and otherdevices, a combination of correction steps, and the like.

Gas Sensors Calibration Examples

The below examples illustrate some possible implementation scenarios ofthe calibration process and example capabilities of the air qualitymonitoring system.

Calibration Example 1

A low-precision ozone sensor is sensitive to rapid changes in humidityand nitrogen dioxide. Using a high precision instrument, the processcalculates sensitivity of the sensor to ozone, humidity changes, andnitrogen dioxide. The process uses these values to eliminate humiditychanges and nitrogen dioxide from the values returned by thelow-precision ozone sensor and deduce the true ozone value.

Calibration Example 2

A low-precision sulfur dioxide sensor is sensitive to changes inhumidity and passing radio waves. The system combines the sulfur dioxidesensor with a humidity sensor, and a hydrogen sulfide sensor withsimilar material properties—hydrogen sulfide (outside of sewers andmarshes) is low in the environment. The process calculates thesensitivity of the hydrogen sulfide sensor to sulfur dioxide and thesensitivity of the sulfur dioxide sensor to humidity changes. When thesulfur dioxide sensor reads high and hydrogen sulfide reads high, thesystem ignores the sulfur dioxide reading, assuming that a passing radiowave is setting off the system. When the sulfur dioxide sensor readshigh and hydrogen sulfur sensor reads low, then a sulfur dioxide readingis confirmed.

Calibration Example 3

A network of air quality monitors is installed in a city. One of the airquality monitors is placed next to a reference monitor withhigh-precision gas sensors. Periodically (e.g., every minute), the errorbetween the air quality monitor and the reference monitor is calculatedand applied as a correction to other air quality monitors in thenetwork.

Source Determination and Action Recommendation Process

Air quality monitoring system 110 is configured to determine sources ofgas that are detected by air quality monitors. An example sourcedetermination method 500 is shown in FIG. 5 . At step 516, user-inputdata related to an environment is determined. The user-input data mayinclude any type of input about the environment, as such conditionsassociated with one or more air quality monitors deployed around alocation. User-input data can include any of a variety of types of datasuch as:

-   -   1. type and location of objects, such as newly installed carpet        that can out-gas chemicals;    -   2. events that can cause chemicals in the air, such as cleaning        using chemical products;    -   3. layout of the location, such as the placement of vents,        windows, and doors; and    -   4. users' personal data, such as allergies, medical conditions,        health concerns, daily routines, travel plans, and the like.

Air quality monitoring system 100 may receive this data in any of avariety of way, such as through a website, an application installed on amobile device, automatically from home sensors or mobile devices,information from other systems and services, and the like.

At step 524, the source determination method 500 determines measurementsfor a target gaseous chemical over a period. These measurements may beprovided by air quality monitors of air quality monitoring system 110.At step 528, additional data associated with the measurements aredetermined. The additional data may include data from a variety ofsources that are relevant to determining sources of gaseous chemicalsmeasured by air quality monitoring system 110. The additional data maycome from any of a variety of sources, such as weather data from weatherstations, traffic data from traffic management administration, chemicalevents such as from government reporting agencies, online services suchas social network, and the like.

At step 532, a plurality of possible sources of the target chemical areidentified based at least in part on the user-input data and additionaldata. At step 536, one or more sources from the plurality of possiblesources of the target chemical are identified by correlation. Thecorrelation may be determined between the measured data from an airquality monitor, user-input data and additional data. For example, thepresence and amount of a gaseous chemical may correlate to an event oran object at the proximate location and at around the same time. Thecorrelation process may be implemented in any of a variety of ways. Anexample process is shown below along with example equations thatillustrate the methodology. Artificial intelligence algorithms andcloud-based data analytics may be employed as part of the correlationprocess.

At step 544, at least one determined source is output. The source may beoutput in any of many different ways, such as data to a service, awebsite, a user-interface on a mobile app and the like. Sourcedetermination method 500 may also provide recommendations, such as toreduce the gaseous chemical from the source, reduce exposure to thegaseous chemical, and the like. Examples are provided in the sourcedetermination examples below.

Correlation Process and Calculations

An example of correlation steps and calculations are provided below:

-   -   1) Sort training data into categories using a clustering        algorithm, such as a k-means clustering approach. Given a set of        d parameters and n observations of each parameter, we solve the        following minimization equation to cluster the data into k        sets S. This is done by finding means mu:

${{argmin}_{s}{\sum\limits_{i = 1}^{k}{\sum\limits_{\chi \in S_{i}}{{\chi - \mu}}^{2}}}} = {{argmin}_{s}{\sum\limits_{i = 1}^{k}{{❘S_{i}❘}\sigma^{2}S_{i}}}}$

-   -   2) In real-time, feed in data. Using the categorization        established in (1), determine which category Si variable x is        most likely to fit into by solving for i:

$\min{\sum\limits_{i = 1}^{k}{\sum\limits_{\chi \in S_{i}}{{\chi - \mu}}^{2}}}$

-   -   3) Map categorization S to solutions S′ using scientific        literature reviews, best practices from experts, and clinical        guidelines.

Source Determination and Action Recommendation Examples

The below examples illustrate some possible implementation scenarios ofthe chemical source differentiation process and example capabilities ofthe air quality monitoring system.

Source Determination and Action Recommendation Example 1

A volatile organic compound (VOC) sensor detects a large, quick increasein VOC concentration that quickly dissipates. By taking into account theconcentration, change of concentration over time, and time of thesignal, the process determines that the source is most likely to be aconsumer cleaning product.

Source Determination and Action Recommendation Example 2

Detecting high VOC concentrations in an indoor environment, the airquality monitoring system recommends that individuals open a window toincrease airflow and reduce their health exposure.

Source Determination and Action Recommendation Example 3

The air quality monitoring system detects high temperature, pressure andozone levels outdoors characteristic of stationary pressure weathersystem during the summertime on the East Coast. The system determinesthat the high ozone levels are most likely due to high levels of ozonebeing blown into the area, coupled with high levels of traffic. Thesystem recommends that the city increase carpooling and publictransportation use.

Source Determination and Action Recommendation Example 4

The air quality monitoring system detects moisture, pressure, and highlevels of particulate matter during an early fall cold spell in thePacific Northwest. It deduces that an inversion layer is responsible forthe buildup in pollution and suggests that the city reduce biomassburning to reduce pollution (e.g., what is colloquially referred to as a‘burn ban’).

Source Determination and Action Recommendation Example 5

The air quality monitoring system detects high levels of particles andnitrogen dioxide in India in the winter. The system recommends thatusers wear a protective mask to lower their health exposure topollution.

Enhanced Gaseous Sensor with Paired Spectrophotometry and Nephelometry

The air quality monitoring system described herein may include anenhanced gaseous chemical sensor configured as a low-maintenancespectrophotometer and nephelometer that identifies gaseous chemicals bytheir light absorption spectrum and particulate matter by theirscattering spectrum. The gaseous chemical sensor described below is agaseous chemical sensing device for measuring chemicals in air. Itincludes a light source that emits light rays and a spectrophotometricdetector. The chemical sensor also includes a cell having two reflectivesurfaces located at opposite ends of the cell. The reflective surfacesare configured to reflect the light rays along a path across the celland to direct the light rays to the spectrophotometric detector. Thiscell enables light rays to pass through the analyte gas multiple timesto enable more accurate measurements and to minimize the interference ofparticulate matter in the analyte gas with respect to thespectroanalysis. The configuration of the cell also enables themeasurement of particulate matter in the analyte gas, for example, asensor that measures light scattered by particulate matter thatintercepts the light rays along the path.

The chemical sensor also includes an analyzer that measures at least onechemical by receiving measurements made by the spectrophotometricdetector. It is configured to determine the amount of specto-absorptiondue to presence of the at least one gaseous chemical and compensate forpresence of the particulate matter based on amount of scattered lightmeasured by the photo detector. An example gaseous chemical sensor thatcan be implemented in an air quality monitor and example components ofthe sensor are illustrated in FIG. 6 and described in more detailedbelow. The example components illustrated in FIG. 6 include:

-   -   I: Gas intake followed by an inertial trap for large particulate        matter    -   0: Gas output (where the analyte is pumped out)    -   S1: Broadband light source    -   A1, A2, A3: Round apertures to improve coherence    -   L1, L2, L3: Convex lenses for the collimator/concentrator        system; L3 serves as seal    -   M1: Alignment mirror to inject the collimated rays in the        multi-pass mirror cell    -   M2, M3: pair of flat or concave mirrors forming a multi-pass        mirror cell for path length extension.    -   W1, W2: Observation windows for the spectrometers    -   SP1, SP2: Spectrophotometer sensors; SP2 acts alternately as a        nephelometer

Optical Description

Light is produced by the light source S1 and collimated into (close to)parallel rays by the optical system formed by L1, L2, L3, A1 and A2. Thelight source may have a reflector to improve light concentration. Thechosen layout of lenses and apertures concentrate the collimated lightinto a tighter beam as well. Any other system of optical elements may beused to generate the light collimation and concentration, or otherbroadband sources of collimated light such as a laser comb may beemployed.

The rays of light, concentrated and collimated, are injected in amulti-pass mirror cell. Such a cell may use flat or focusing mirrors M2,M1 such as convex mirrors (as with a Herriott cell, White cell, Pfundcell or circular multi-pass cell). The cell is configured to increasethe pathlength of light passing through the analyte gas. The longerpathlength increases absorption and Signal-to-Noise Ratio (SNR). Therays of light, after exiting the cell, are directed toward thespectrophotometer sensor SP1. This sensor and accompanying digitalcircuitry determine the intensity and spectral distribution of lightthat travelled from the source and through the analyte gas. Sensor SP2analyzes the spectrum and intensity of light rays scattered by anyparticulate matter present in the analyte gas traveling through thesystem from I to 0. Micro-spectrophotometers, such as a Fabry-PerotInterferometer (FPI, such as those made by the manufacturer Infratec) ordiffraction grating-based micro-spectrophotometers (such as those madeby manufacturer Hamamatsu) among others, may be suitable for SP1 andSP2.

Structural Description

The body of the device separates the optics and electronics from theenvironment, exposes the optical cell to the pumped gas analyte, rigidlymaintains alignment of the optical elements, and limits the influence ofstray light rays on the light spectrum emitted by S1. The body of thedevice can be built as a single structural unit with top and bottomplates for sealing. The body may be built out of various rigidmaterials, and can be 3d-printed, machined, molded, injected, extruded,or produced through other suitable processes. Interface elements areused to connect the optical elements to the body. The structure may alsobe optimized to limit the upper bound of particulate matter in thedevice body without placing filters on the input analyte gas channel.

For sealing, gaskets may be used between the mirror cell (exposed to theanalyte gas), the environment and the optics and electronics (maintainedin a dry atmosphere). Certain optical elements, such as the observationwindows W1 and W2 and the lens L3, provide as an interface between theoptical chamber and the rest of the system, and may be used as seals.

For optics alignment, the structure is rigidly constructed and may usesymmetries, topology optimization and low thermal expansion materials tolimit misalignment of the optical elements during the lifetime of thedevice. Interface elements in the form of adjustable mounting plates actas optical holders for precise alignment and calibration. These platesmay be machined to fit the optical elements precisely and may be linkedto the main body with screws, bolts or other fastening systems. Theplates are set in place in the body such that the described optical pathis realized, and their positions can be calibrated at assembly andduring periodic maintenance.

For stray light ray limitation, the entire body of the device in theoptical cell may be coated with a light-absorbing coating with lowreflectivity in the observational band of interest. This can be achievedby a variety of ways, such as by anodization, by deposition, or bypainting a coating using carbon black, nigrosin, black oxides of variousmetals (such as aluminum, zinc, or platinum), graphene, graphite, carbonnanotubes or flurenes, felt or various other materials. The structuremay further integrate optical baffles and reflection traps that canlimit stray rays reaching SP1 or SP2.

For passive, filter-less clogging avoidance, an inertial trap can beused by taking advantage of the conservation of linear or angularmomentum. Vortex-like traps can be used to force larger particulate outfor high-speed pumping rate, and meander-like traps (such as thephysical element proximal to I in the schematic) can be used with alow-speed pumping rate. These structures can be added to serve aspassive filters of large particulate matter in order to extendoperations of the device between routine maintenance servicing.

Functional Description of Gas Identification

The analyte gas may be pumped into the spectrometer through I and mayexit the device through O. Pumping can be performed with any type of fanor pump. The pump may be located on the output line to provide laminarflow and pumps the gas out from O.

The analyte gas contains trace gases to be identified by thespectrometer. While passing in the spectrometer, the analyte gasintercepts rays of light and absorbs specific wavelength, which dependupon the type and concentration of gases in the sample. Further, theanalyte gas may contain a heterogeneous suspension of particulate matterwhich intercept, absorb and scatter light. The scattering depends uponthe size of the particulate and the absorbance depends upon the albedoand geometry of the various individual particles in the heterogenousparticulate suspension. By observing the spectrum of the transmittedlight and the spectrum of the scattered light, information relevant tothe gas type and concentration and shape, size and albedo of theparticulate matter can be inferred.

Signal Processing

The raw information gathered by the proposed system is in the form oflight intensity with respect to wavelength and time from thespectrometers SP1 and SP2. These signals, called spectra, depend on theproperties of the analyte gas and particulate matters, the light source,the optical system and the spectrometer sensor properties. By using theknown properties of the light source and the optical system, the signalis first processed. The optical properties of the specific spectrometersensor used are also used to further refine the signal. For the case ofFPI sensors, the information about the Fabry-Perot Interferometertransfer function is used to apply a signal deconvolution (orconvolution of the reference solution) and enhance the sensorsensitivity when compared to the manufacturer-reported resolution.

For trace gas detection, the spectrum values from sensor SP2 is used tofurther enhance the spectrum data obtained by sensor SP1. Some of thespectrum of SP1 that may be attributed to absorption may in fact be theresult of scattering, and the scattered light spectrum obtained by SP2can be used to assist interpretation of the signal from SP1. Ad hocknowledge about the probable analyte content using the data analyticsmethods described herein, together with the known typical spectralsignatures for various gases at ambient environmental concentrations mayused to assist with interpretation of the spectrometric data intoprobable gas mixtures and concentrations.

For particulate matter detection, the spectrum values from SP2 are usedto identify characteristics of the particulate, such as type, size,shape and albedo. The particulate matter scattering spectrum can be usedas a unique fingerprint for a particular type of particulate. Thealignment of sensor SP2 in the optical system is standard for alight-scattering detector known as a nephelometer. However,nephelometers do not use broad spectrum analysis and rely on aninference of shape and albedo to analyze scattered light. By using abroadband spectrum for light source S 1, more information can be gainedon the size, shape and albedo of the particulate matter. Acquiring alibrary of spectral responses for various particulate type, size, shapeand albedo can be used to identify the probable mixtures of gases andparticulate matter at ambient environmental concentration in the sample.

Example of Signal Processing for Methane Monitoring

The analyte gas contains an inert gas, water vapor, carbon dioxide andmethane as well as particulate matter. The spectra from SP1 and SP2 arecollected, as well as the temperature and pressure of the chamber. Thesignals SP1 and SP2 are first deconvoluted from the known transferfunction of the optical system and the light source at the knowntemperature and pressure of the chamber. The transmission spectrum fromSP1 is compensated by the scattered spectrum of SP2. Similarly, thetransmission spectrum from SP1 is used to compensate the scatteredspectrum of SP2. The individual reference spectra at known concentrationof water vapor, carbon dioxide and methane may be recovered from our owninvestigations or from a public database. The known transfer function ofSP1 is applied as a convolution to the reference spectra of water vapor,carbon dioxide and methane. Our algorithm generates synthetic spectra ofthe water vapor, carbon dioxide and methane mixture from the convolutedreference spectra for various concentrations at the measured temperatureand pressure. By minimizing the difference of the generated syntheticspectra and the actual refined spectrum from SP1, the actualconcentrations of water, carbon dioxide and methane are found.

Specific details are given in the above description to provide athorough understanding of the embodiments. However, it is understoodthat the embodiments may be practiced without these specific details.For example, circuits may be shown in block diagrams in order not toobscure the embodiments in unnecessary detail. In other instances,well-known circuits, processes, algorithms, structures, and techniquesmay be shown without unnecessary detail in order to avoid obscuring theembodiments.

Also, it is noted that the embodiments may be described as a processwhich is depicted as a flowchart, a flow diagram, a swim diagram, a dataflow diagram, a structure diagram, or a block diagram. Although adepiction may describe the operations as a sequential process, many ofthe operations can be performed in parallel or concurrently. Inaddition, the order of the operations may be re-arranged. A process isterminated when its operations are completed, but could have additionalsteps not included in the figure. A process may correspond to a method,a function, a procedure, a subroutine, a subprogram, etc. When a processcorresponds to a function, its termination corresponds to a return ofthe function to the calling function or the main function.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly or conventionally understood. As usedherein, the articles “a” and “an” refer to one or to more than one(i.e., to at least one) of the grammatical object of the article. By wayof example, “an element” means one element or more than one element.“About” and/or “approximately” as used herein when referring to ameasurable value such as an amount, a temporal duration, and the like,encompasses variations of ±20% or ±10%, ±5%, or +0.1% from the specifiedvalue, as such variations are appropriate to in the context of thesystems, devices, circuits, methods, and other implementations describedherein. “Substantially” as used herein when referring to a measurablevalue such as an amount, a temporal duration, a physical attribute (suchas frequency), and the like, also encompasses variations of ±20% or±10%, ±5%, or +0.1% from the specified value, as such variations areappropriate to in the context of the systems, devices, circuits,methods, and other implementations described herein.

As used herein, including in the claims, “and” as used in a list ofitems prefaced by “at least one of” or “one or more of” indicates thatany combination of the listed items may be used. For example, a list of“at least one of A, B, and C” includes any of the combinations A or B orC or AB or AC or BC and/or ABC (i.e., A and B and C). Furthermore, tothe extent more than one occurrence or use of the items A, B, or C ispossible, multiple uses of A, B, and/or C may form part of thecontemplated combinations. For example, a list of “at least one of A, B,and C” may also include AA, AAB, AAA, BB, etc.

While illustrative and presently preferred embodiments of the disclosedsystems, methods, and/or machine-readable media have been described indetail herein, it is to be understood that the inventive concepts may beotherwise variously embodied and employed, and that the appended claimsare intended to be construed to include such variations, except aslimited by the prior art. While the principles of the disclosure havebeen described above in connection with specific apparatuses andmethods, it is to be clearly understood that this description is madeonly by way of example and not as limitation on the scope of thedisclosure.

What is claimed is:
 1. An air quality monitoring method comprising:accepting analyte gas into a cell coated with: a light-absorbingcoating; emitting light rays into the analyte gas; reflecting the lightrays into the analyte gas repeatedly across the cell; directing thelight rays across a path within the cell into at least one sensor inresponse to repeated reflections; absorbing, by the light-absorbingcoating of the cell, stray light rays from the light rays emitted intothe analyte gas; generating a first spectra signal and a second spectrasignal in response to directing the light rays into the at least onesensor; deconvoluting the first spectra signal and the second spectrasignal based on properties of a light source emitting the light rays andoptical properties of the at least one sensor at current temperature andpressure within the cell to generate a deconvoluted first spectra signaland a deconvoluted second spectra signal; and determining a gaseouschemical based on the deconvoluted first spectra signal.
 2. The airquality monitoring method of claim 1, wherein the cell comprises atleast one of: an optical baffle, and a reflection trap, wherein theoptical baffle and the reflection trap are configured to absorb thestray light rays emitted into the analyte gas.
 3. The air qualitymonitoring method of claim 2 and further comprising: collimating thelight rays prior to emitting into the analyte gas; and concentrating thelight rays in response to collimating, prior to emitting the light raysinto the analyte gas.
 4. The air quality monitoring method of claim 1and further comprising: pumping the analyte gas, via one of at least onepump, into the cell; and pumping the analyte gas, via one of at leastone pump, out of the cell, wherein the at least one pump provide laminarflow to the analyte gas within the cell.
 5. The air quality monitoringmethod of claim 1, wherein reflecting and directing the light rays alongthe path increases spectra-absorption and Signal to Noise (SNR) Ratio.6. The air quality monitoring method of claim 1, wherein the cellcomprises at least two reflective surfaces at opposite ends to enablereflecting the light rays repeatedly into the cell.
 7. The air qualitymonitoring method of claim 1, wherein the at least one sensor is aspectrophotometric detector.
 8. The air quality monitoring method ofclaim 1, wherein the at least one sensor is a nephelometer detector. 9.The air quality monitoring method of claim 1 further comprising:receiving a reference spectra at predetermined concentration of each ofwater vapor, carbon dioxide and methane from a database; applyingtransfer function of the first spectra signal as a convolution to thereference spectra of each of water vapor, carbon dioxide, and methane,to generate convoluted reference spectra; generating synthetic spectraof each of water vapor, carbon dioxide and methane from the convolutedreference spectra for a plurality of concentrations at a measuredtemperature and a measured pressure; and determining an actualconcentration of each of water, carbon dioxide and methane, byminimizing difference of generated synthetic spectra and an actualspectrum from the first spectra signal.
 10. An air quality monitoringmethod comprising: accepting analyte gas into a cell coated with: alight-absorbing coating; emitting light rays into the analyte gas;reflecting the light rays into the analyte gas repeatedly across thecell; directing the light rays across a path within the cell into atleast one sensor in response to repeated reflections; absorbing, by thelight-absorbing coating on the cell, stray light rays from the lightrays emitted into the analyte gas; generating a first spectra signal anda second spectra signal in response to directing the light rays into theat least one sensor; deconvoluting the first spectra signal and thesecond spectra signal based on properties of a light source emitting thelight rays and optical properties of the at least one sensor at currenttemperature and pressure within the cell to generate a deconvolutedfirst spectra signal and a deconvoluted second spectra signal; andidentifying a particulate matter based on the deconvoluted secondspectra signal.
 11. The air quality monitoring method of claim 10,wherein the cell comprises at least one of: an optical baffle, and areflection trap, wherein the optical baffle and the reflection trap areconfigured to absorb the stray light rays emitted into the analyte gas.12. The air quality monitoring method of claim 10, wherein the at leastone sensor is a nephelometer detector.
 13. The air quality monitoringmethod of claim 10, wherein the at least one sensor is aspectrophotometric detector.
 14. The air quality monitoring method ofclaim 10, wherein identifying the particulate matter comprisesdetermining at least one of a shape, a size, and an albedo associatedwith the particulate matter.
 15. A system for air quality monitoring,the system comprising: a light source that emits light rays; a cell toaccept light rays emitted from the light source, wherein the cell iscoated with: a light-absorbing coating; wherein the cell accepts analytegas, and the cell comprising: two reflective surfaces located atopposite ends of the cell; wherein the two reflective surfaces are torepeatedly reflect the light rays along a path across the cell and todirect the light rays into at least one sensor in response to repeatedreflections; and wherein the light-absorbing coating is configured toabsorb stray light rays emitted from the light source; wherein a firstspectra signal and a second spectra signal is generated in response todirecting the light rays into the at least one sensor; and a processingmodule to: deconvolute the first spectra signal and the second spectrasignal based on properties of the light source emitting the light raysand optical properties of the at least one sensor at current temperatureand pressure within the cell to generate a deconvoluted first spectrasignal and a deconvoluted second spectra signal; and determine a gaseouschemical based on the deconvoluted first spectra signal.
 16. The systemof claim 15, wherein the cell comprises at least one of: an opticalbaffle, and a reflection trap, wherein the optical baffle and thereflection trap are configured to absorb stray light rays from the lightrays emitted into the analyte gas.
 17. The system of claim 15, furthercomprising: at least one pump for: pumping the analyte gas into thecell; and pumping the analyte gas out of the cell, wherein the at leastone pump provide laminar flow to the analyte gas within the cell. 18.The system of claim 15, wherein the at least one sensor is aspectrophotometric detector.
 19. The system of claim 15, wherein the atleast one sensor is a nephelometer detector.
 20. A system for airquality monitoring, the system comprising: a light source that emitslight rays; a cell to accept light rays emitted from the light source,wherein the cell is coated with: a light-absorbing coating; wherein thecell accepts analyte gas, the cell comprising: two reflective surfaceslocated at opposite ends of the cell; wherein the two reflectivesurfaces are to repeatedly reflect the light rays along a path acrossthe cell and to direct the light rays into at least one sensor inresponse to repeated reflections; and wherein the light-absorbingcoating is configured to absorb stray light rays emitted from the lightsource; wherein a first spectra signal and a second spectra signal isgenerated in response to directing the light rays into the at least onesensor, and a processing module to: deconvolute the first spectra signaland the second spectra signal based on properties of the light sourceemitting the light rays and optical properties of the at least onesensor at current temperature and pressure within the cell to generate adeconvoluted first spectra signal and a deconvoluted second spectrasignal; and identify particulate matter based on the deconvoluted secondspectra signal.
 21. The system of claim 20, wherein the cell comprisesat least one of: an optical baffle, and a reflection trap, wherein theoptical baffle and the reflection trap are configured to absorb straylight rays from the light rays emitted into the analyte gas.
 22. Thesystem of claim 20, wherein identifying the particulate mattercomprises: determining at least one of a shape, a size, and an albedoassociated with the particulate matter.
 23. The system of claim 22,wherein the processing module is to: acquire a library of spectralresponses for a type, the size, the shape, and the albedo associatedwith the particulate matter; and identify a probable mixture of gasesand particulate matter at ambient environmental concentration in asample, using the library of spectral responses.
 24. The system of claim20, wherein the at least one sensor is one of: a nephelometer detector;and a spectrophotometric detector.